Broadcast technologies often provide a single broadcast format to all listeners or viewers. For example, a radio station provides the same music or program to all listeners at the same time. One problem with this format is that most people want to listen only to music that they like. Radio stations attempt to satisfy most listeners by broadcasting music via in accordance with a varied playlist. However, the same listeners switch from one music station to another to get diversity when a song is played that they do not like. Traditional over-the-air stations are broadcast from one transmitter to many receivers, so personalization is not possible other than for blanket categories, such as the type of music, e.g., pop, classical, jazz, etc. In Internet radio, one-to-one personalization is more achievable. However, a dedicated channel from source to destination is required. This makes the cost of personalized Internet radio very high.
By broadcasting N radio stations for M users, where N/M is <1, some amount of personalization is achieved at the expense of the bandwidth to broadcast N playlists. The amount of bandwidth required is N*BW*M, where N is the number of radio stations, BW is the amount of bandwidth required per user and M is the number of users.
Utilizing multicasting in future systems may approach the bandwidth requirements needed for personalization (N*BW). In multicasting, the radio stations can send a single stream out to the world and the individual receiver removes personal preferences or selected data from the stream. The limitation in this scheme is that there are still only N available radio stations. For true personalization, the goal is to permit N to approach the value of M (the number of users).
One model for content delivery that is employed today is by streaming media files (such as MP3s or REALAUDIO™). Streaming servers have been deployed around a global network to enable real time delivery of content. This model typically does not allow true personalization since the same stream is being served to many people. A second model actually downloads files from a cached server. The cached music can minimize overall latency and network congestion, but in the end, users are required to perform a separate download. This model uses a lot of bandwidth, as well.
Therefore, a need exists for a method and system, which achieves personalization without the high bandwidth and server costs associated with the prior art.